AI is transforming investment research, but without structured workflows and auditing, outputs remain unreliable—driving a shift toward system-driven analysis.
Cohres Ltd. announced insights into the evolving role of artificial intelligence in investment research, highlighting the increasing importance of structured workflows and AI auditing in delivering reliable, decision-ready analysis.
The future of investment research is not just AI-powered, it is structured, audited, and system-driven to ensure reliable and decision-ready insights”
— Daniel Nikic, Founder of Cohres Ltd.
The rapid expansion of global data is projected to exceed 220 zettabytes annually and has fundamentally changed how information is processed and analyzed. In investment research, the challenge is no longer access to data, but the ability to efficiently interpret it and extract actionable insights. (Exploding Topics, February 2026)
It is evident from conversations across my global network that AI adoption is accelerating in investment research. Investment teams are increasingly leveraging AI to improve speed, scale, and efficiency across research processes.
The analysis highlights that relying solely on AI is insufficient for investment decision-making.
AI Is Not Replacing Investment Research
Those who have used or developed AI systems understand that outputs are not always accurate. AI models are only as reliable as the data they are trained on and the context in which they are applied. As a result, outputs can be inconsistent and, at times, inaccurate, including risks of hallucination or misinterpretation.
While AI can process large volumes of data significantly faster than humans, speed without verification introduces risk into investment decision-making.
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The Importance of AI Auditing
Nothing is perfect—neither humans nor AI systems. AI auditing refers to the systematic process of reviewing, validating, and structuring AI-generated outputs to ensure accuracy, relevance, and decision-readiness. Based on my experience developing and using AI tools, auditing is a critical component in making AI outputs usable in real-world investment scenarios.
This need for verification is increasingly recognized by leading AI experts:
Demis Hassabis (CEO, Google DeepMind)
“Current tools get too many obvious questions wrong and contain too many ‘holes,’ which is why you still have to carefully check what they produce.”
(Source: TechCrunch, May 2025 – https://techcrunch.com/2025/05/22/anthropic-ceo-claims-ai-models-hallucinate-less-than-humans/)
Dario Amodei (CEO, Anthropic)
“As powerful as these systems are, you should still verify the answer to something very specific and get a second human opinion.”
(Source: TechCrunch, May 2025 – https://techcrunch.com/2025/05/22/anthropic-ceo-claims-ai-models-hallucinate-less-than-humans/)
Corporate communications guidance on AI (Haiilo)
“In an age where AI touches almost every aspect of our lives, the old adage ‘trust but verify’ has never been more relevant… we must provide human oversight to AI-generated output and ensure its appropriateness.”
(Source: Haiilo, March 2024 – https://blog.haiilo.com/blog/trust-but-verify/)
The findings emphasize that AI remains a powerful tool, but requires proper validation.
The report outlines a structured framework::
– AI generates raw output
– Workflows structure and organize the information
– Auditing verifies accuracy and ensures quality
This creates a simple but effective framework: AI + Workflow + Auditing
This integrated approach can be defined as an AI Concierge system.
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